We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
PURITAN MEDICAL

Download Mobile App




New AI-Based Method Improves Diagnosis of Drug-Resistant Infections

By LabMedica International staff writers
Posted on 09 Apr 2025

Drug-resistant infections, particularly those caused by deadly bacteria like tuberculosis and staphylococcus, are rapidly emerging as a global health emergency. More...

These infections are more difficult to treat, often necessitate costlier or more toxic medications, and lead to extended hospital stays and higher mortality rates. In 2021, the World Health Organization (WHO) reported that 450,000 people developed multidrug-resistant tuberculosis, with a treatment success rate falling to just 57%. Current resistance detection methods, employed by organizations such as the WHO, either take too long—such as culture-based testing—or fail to detect rare mutations, as seen with some DNA-based tests. Now, a new artificial intelligence (AI)-based method has been developed to more accurately detect genetic markers of antibiotic resistance in Mycobacterium tuberculosis and Staphylococcus aureus, which could facilitate faster and more effective treatment.

Researchers at Tulane University (New Orleans, LA, USA) have introduced an innovative Group Association Model (GAM), leveraging machine learning to identify genetic mutations associated with drug resistance. Unlike traditional tools that might mistakenly link unrelated mutations to resistance, GAM operates without relying on prior knowledge of resistance mechanisms, making it more adaptable and capable of identifying previously undetected genetic alterations. The model, detailed in Nature Communications, addresses both the slow diagnostic processes and the failure to detect rare mutations by analyzing whole genome sequences. It compares groups of bacterial strains with varying resistance profiles to identify genetic changes that consistently indicate resistance to specific drugs.

In their study, the researchers applied GAM to over 7,000 strains of Mtb and nearly 4,000 strains of S. aureus, identifying crucial mutations linked to resistance. They discovered that GAM not only matched or surpassed the accuracy of the WHO’s resistance database but also significantly reduced false positives, which are incorrect markers of resistance that could lead to improper treatment. The combination of machine learning with GAM also enhanced its predictive capabilities, particularly when working with limited or incomplete data. In validation tests using clinical samples from China, the machine-learning-enhanced model outperformed the WHO-based methods in predicting resistance to critical first-line antibiotics. This breakthrough is important because early detection of resistance allows doctors to adjust treatment regimens appropriately, preventing the infection from worsening or spreading. The model's ability to identify resistance without requiring expert-defined rules also suggests it could be applied to other bacterial infections.

“Current genetic tests might wrongly classify bacteria as resistant, affecting patient care,” said lead author Julian Saliba, a graduate student in the Tulane University Center for Cellular and Molecular Diagnostics. “Our method provides a clearer picture of which mutations actually cause resistance, reducing misdiagnoses and unnecessary changes to treatment.”


New
Gold Member
Serological Pipets
INTEGRA Serological Pipets
Serological Pipet Controller
PIPETBOY GENIUS
New
ESR Analyzer
TEST1 2.0
New
Silver Member
Quality Control Material
NATtrol Chlamydia trachomatis Positive Control
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








DIASOURCE (A Biovendor Company)

Channels

Molecular Diagnostics

view channel
Image: A diagnostic test can distinguish patients with head and neck squamous cell carcinoma who can be cured with surgery alone (Photo courtesy of University of Turku)

Novel Diagnostic Tool to Revolutionize Treatment Guidance of Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) is a solid tumor type commonly treated with surgery. However, there has been no clinically available method to determine which patients can be cured with surgery... Read more

Hematology

view channel
Image: The microfluidic device for passive separation of platelet-rich plasma from whole blood (Photo courtesy of University of the Basque Country)

Portable and Disposable Device Obtains Platelet-Rich Plasma Without Complex Equipment

Platelet-rich plasma (PRP) plays a crucial role in regenerative medicine due to its ability to accelerate healing and repair tissue. However, obtaining PRP traditionally requires expensive centrifugation... Read more

Immunology

view channel
Image: The 3D paper-based analytical device has shown high clinical accuracy for adult-onset immunodeficiency (Photo courtesy of National Taiwan University)

Paper-Based Device Accurately Detects Immune Defects in 10 Minutes

Patients with hidden immune defects are especially vulnerable to severe and persistent infections, often due to autoantibodies that block interferon-gamma (IFN-γ), a key molecule in immune defense.... Read more

Technology

view channel
Image: The Check4 gene-detection platform (Photo courtesy of IdentifySensors)

Electronic Biosensors Used to Detect Pathogens Can Rapidly Detect Cancer Cells

A major challenge in healthcare is the early and affordable detection of serious diseases such as cancer. Early diagnosis remains difficult due to the complexity of identifying specific genetic markers... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.